Mass Apprasial With A Machine Learning Algorithm: Random Forest Regression

Abstract

Traditional methods in many areas have been replaced by modern methods known as machine learning with the rapidly developing technology and innovations in science. One of these areas is real estate valuation (appraisal) area. Real estate appraisal can be conducted on a single real estate as well as appraisal of more than one real estate together, which is called as mass appraisal, is possible. In this study, a mass appraisal is performed by a Random Forest Regression method, and the results were evaluated. For this purpose, data of 189 flats expected real value and their 13 variables were collected in Yenimahalle, Ankara. 75% of these data were used as training data and 25% as test data. According to the results, a difference of at minimum 600 TL, maximum 60.000 TL and averagely 25.000 TL were observed between the predicted value by the Random Forest regression and the expected real value. According to these results, random forest regression is a successful method in mass appraisal, and it is observed that valuation with different machine learning methods such as random forest regression has a positive effect on time and labor force comparing with valuation of real estate by traditional methods individually.